Statistician - Manufacturing and Analytics (ADX-369-20)

The Manufacturing Statistician will contribute key statistical expertise to work with our rapidly growing Manufacturing and Quality Analytics program for multiple next-generation sequencing diagnostic assays. The successful applicant will apply statistical methodology to a variety of design, process improvement, and development activities. This person will be part of the biostatistics group and will assist engineers and other managers on appropriate statistical methods. You will work closely with fellow statisticians, engineers, technical leads, and quality assurance personnel to think creatively and achieve ambitious goals to advance the treatment of cancer and ensure products are reliably of the highest quality.

RESPONSIBILITIES:

• Use a wide variety of rigorous statistical methods/techniques to understand and improve the quality of in-vitro diagnostic kit reliability.
• Investigates and analyzes sources of variability potentially effecting product reliability as dictated by the company, our customers, or regulatory standards.
• Communicate statistical ideas, concepts, and analytic results with key stakeholders both orally and in writing.
• Develop source code for statistical analysis in JMP or R and conduct statistical studies to identify sources of variation in manufacturing processes.
• Adhere to standard operating procedures (including proper documentation and version control) and work in a manner compliant with regulatory standards.
• Work effectively and collaboratively as a member of a multidisciplinary team. May partner on analytical validation studies and establishing appropriate assay acceptance criteria.

EXPERIENCE:

• 3+ years of relevant industry experience in statistics, applied statistics, or related field.

Experience or familiarity with multiple of the following statistical concepts/methods/techniques:

• Multivariate analysis (cluster analysis, factor analysis, PCA, MANOVA, discriminant analysis etc.)
• Multivariate statistical process control
• Time series analysis
• Power analyses
• DOE and response surface methodology
• Mixed models, generalized regression techniques, spline/piece-wise regression
• Bayesian methods and statistical simulations
• Predictive modeling methods such as machine learning, neural network, decision trees, etc.
• Data visualization techniques
• Graduate coursework in statistical theory as well as strong empirical skills.
• Knowledge of and experience working within FDA or other regulatory environments a plus
• Proficiency with JMP and R
• Ability to create visualizations that effectively communicate statistical results
• Understanding of molecular biology/genomics a plus
• Excellent communication skills and ability to explain complex concepts to non-experts
• Ability to meet goals in a fast-paced environment
• Excellent communication